Fast Variational Level Set Based Image Segmentation via Two-Scale Filtering Model

Abstract

One major difficulty in medical image segmentation is intensity inhomogeneity, which manifests itself with a slow intensity variation over the whole image domain. Recently, a local binary fitting (LBF) model has been proposed to solve this problem within level set segmentation framework. However, the LBF model has two main problems, i.e., high computational cost and sensitivity to initialization. By analyzing the LBF model, we find that the most computational part is the calculation of two cluster images, which need to be updated in each iteration during the evolution of level set function. With this observation in mind, we propose a novel two-scale filtering (TSF) model, in which the two cluster images can be pre-calculated before evolution. Additionally, we implicitly utilize order constraint to restrict the order of two cluster images. As a result, the proposed TSF model is less sensitive to initialization. Extensive experiments on real medical images illustrate the desirable performances, as compared with the state-of-the-art models.